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Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
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Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
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Pan P, Li J, Wang B, Tan X, Yin H, Han Y, Wang H, Shi X, Li X, Xie C, Chen L, Chen L, Bai Y, Li Z, Tian G. Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis. Front Oncol 2023; 13:1067849. [PMID: 37546388 PMCID: PMC10401844 DOI: 10.3389/fonc.2023.1067849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/21/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.
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Affiliation(s)
- Peng Pan
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China
| | - Bo Wang
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoyan Tan
- Department of Gastroenterology, Maoming People's Hospital, Maoming, China
| | - Hekun Yin
- Department of Gastroenterology, Jiangmen Central Hospital, Jiangmen, China
| | - Yingmin Han
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Haobin Wang
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoshuang Li
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Cuinan Xie
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Longfei Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Lanyou Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Yu Bai
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Geng Tian
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
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Massoud TF, Paulmurugan R. Molecular Imaging of Protein–Protein Interactions and Protein Folding. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00071-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Lin H. Computational Method in Protein Structure and Function Data. Protein Pept Lett 2020; 27:257-258. [DOI: 10.2174/092986652704200311123651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Hao Lin
- Center for Informational Biology University of Electronic Science and Technology of China Chengdu, China
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